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Round-based Super-Individuals – Balancing Speed and Accuracy

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https://dl.acm.org/citation.cfm?id=3316480.3322894
Original languageEnglish
Title of host publicationProceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
Subtitle of host publicationSIGSIM-PADS'19
PublisherACM
Pages95-98
Number of pages4
ISBN (Electronic)978-1-4503-6723-3
DOIs
Publication statusPublished - 29 May 2019
Event2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation - Chicago, United States
Duration: 3 Jun 20196 Jun 2019
http://www.acm-sigsim-pads.org/

Conference

Conference2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
Abbreviated titleSIGSIM PADS 2019
CountryUnited States
CityChicago
Period3/06/196/06/19
Internet address

Abstract

Agent- or individual-based models which are based on a continuous-time Markov chain semantics are increasingly receiving attention insimulation. To reduce computational cost, model aggregation techniques based on Markov chain lumping can be leveraged. However, for models with nested, attributed agents, and arbitrary functions determining their dynamics it is not trivial to find a partition that satisfies the lumpability conditions. Thus, we exploit the potential of the so-called super-individual approaches where sub-populations of agents are approximated by representatives based on some criteria for similarity, and propose a round-based execution scheme to balance speed and accuracy of the simulations. For realization we use an expressive rule-based modeling and simulation framework, evaluate the performance using a fish habitat model, and discuss open questions for future research.

Event

2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

3/06/196/06/19

Chicago, United States

Event: Conference

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